Geographical psychology

Opportunity to view the starry night sky is linked to human emotion and behavioral interest in astronomy

Prior to the modern era, the stars in the night sky were readily visible across the globe, but light pollution has created disparities in the opportunity to see these astronomical objects with the naked eye. This alteration may measurably impact human behavior. We hypothesize that light pollution is related to the development of people's interest in astronomy, which often serves as a "gateway" to science more broadly. In a state-by-state analysis, we used location information to examine astronomy interest data for millions of US residents. Results show that, among populations with low light pollution, a feeling of "wonder about the universe" is prevalent (r = 0.50). We found that this human emotion mediates the association between low light pollution and behavioral interest in astronomy. Although the effects of light pollution on astronomy, biology, ecology, and health are well-known, the present work demonstrates that light pollution is also relevant to human scientific behavior, with broad implications for science education and society.

Quality of place as the winner of the third wave of the COVID-19 pandemic in terms of quality of life. Will this knowledge strengthen the development of geographical psychology?

The paper is focused on the third wave of the pandemic and its comparison with the first wave in terms of the quality of life of university students in Czechia. In the first wave, the society came together, with solidarity being a prominent manifestation. The third wave differed from the first one in medical (vaccination was already available) as well as social terms. The paper has two objectives, the first is to measure the quality of life and related variables in the third wave of the pandemic and to compare the values found in the third wave with those in the first wave of the pandemic. The second objective is to identify which of the factors related to quality of life are predictors of that quality. The hypothesis assumes different measured values of quality of life for men and women. The measurement yielded knowledge of the high value of quality of place and environmental quality, which can enrich the quality of life epistemology on the one hand and geographical psychology on the other. Trust, health, happiness, quality of place, and safety were identified as factors. The measurement revealed the finding of different quality of life values in the third and first wave of the pandemic. While quality of life values increased in the first wave compared to the pre-pandemic period, both quality of life values and factors decreased in the third wave compared to the first wave. The only exception was one factor that we consider to be a winner of the third wave of the pandemic. The factor that declined in all measurements is considered to be the loser of the third wave of the pandemic. The paper concludes with implications derived from these findings.

Homo temporus: Seasonal Cycles as a Fundamental Source of Variation in Human Psychology

Many animal species exhibit seasonal changes in their physiology and behavior. Yet despite ample evidence that humans are also responsive to seasons, the impact of seasonal changes on human psychology is underappreciated relative to other sources of variation (e.g., personality, culture, development). This is unfortunate because seasonal variation has potentially profound conceptual, empirical, methodological, and practical implications. Here, we encourage a more systematic and comprehensive collective effort to document and understand the many ways in which seasons influence human psychology. We provide an illustrative summary of empirical evidence showing that seasons impact a wide range of affective, cognitive, and behavioral phenomena. We then articulate a conceptual framework that outlines a set of causal mechanisms through which seasons can influence human psychology-mechanisms that reflect seasonal changes not only in meteorological variables but also in ecological and sociocultural variables. This framework may be useful for integrating many different seasonal effects that have already been empirically documented and for generating new hypotheses about additional seasonal effects that have not yet received empirical attention. The article closes with a section that provides practical suggestions to facilitate greater appreciation for, and systematic study of, seasons as a fundamental source of variation in human psychology.

Filling in the white space: Spatial interpolation with Gaussian processes and social media data

Full national coverage below the state level is difficult to attain through survey-based data collection. Even the largest survey-based data collections, such as the CDC's Behavioral Risk Factor Surveillance System or the Gallup-Healthways Well-being Index (both with more than 300,000 responses p.a.) only allow for the estimation of annual averages for about 260 out of roughly U.S. 3,000 counties when a threshold of 300 responses per county is used. Using a relatively high threshold of 300 responses gives substantially higher convergent validity-higher correlations with health variables-than lower thresholds but covers a reduced and biased sample of the population. We present principled methods to interpolate spatial estimates and show that including large-scale geotagged social media data can increase interpolation accuracy. In this work, we focus on Gallup-reported life satisfaction, a widely-used measure of subjective well-being. We use Gaussian Processes (GP), a formal Bayesian model, to interpolate life satisfaction, which we optimally combine with estimates from low-count data. We interpolate over several spaces (geographic and socioeconomic) and extend these evaluations to the space created by variables encoding language frequencies of approximately 6 million geotagged Twitter users. We find that Twitter language use can serve as a rough aggregate measure of socioeconomic and cultural similarity, and improves upon estimates derived from a wide variety of socioeconomic, demographic, and geographic similarity measures. We show that applying Gaussian Processes to the limited Gallup data allows us to generate estimates for a much larger number of counties while maintaining the same level of convergent validity with external criteria (i.e., N = 1,133 vs. 2,954 counties). This work suggests that spatial coverage of psychological variables can be reliably extended through Bayesian techniques while maintaining out-of-sample prediction accuracy and that Twitter language adds important information about cultural similarity over and above traditional socio-demographic and geographic similarity measures. Finally, to facilitate the adoption of these methods, we have also open-sourced an online tool that researchers can freely use to interpolate their data across geographies.

Enriching Psychology by Zooming Out to General Mindsets and Practices in Natural Habitats

Psychology has been "zooming in" on individuals, dyads, and groups with a narrow lens to the exclusion of "zooming out," which involves placing the targeted phenomena within more distal layers of influential context. Here, we plea for a paradigm shift. Specifically, we showcase largely hidden scientific benefits of zooming out by discussing worldwide evidence on inhabitants' habitual adaptations to colder-than-temperate and hotter-than-temperate habitats. These exhibits reveal two different types of theories. Clement-climate perspectives emphasize that generic common properties of stresses from cold and hot temperatures elicit similar effects on personality traits and psychosocial functioning. Cold-versus-heat perspectives emphasize that specific unique properties of stresses from cold and hot habitats elicit different effects on phenomena, such as speech practices and intergroup discrimination. Both zooming-out perspectives are then integrated into a complementary framework that helps identify explanatory mechanisms and demonstrates the broader added value of embedding zooming-in approaches within zooming-out approaches. Indeed, zooming out enriches psychology.

Will Social Distancing in Service Encounters Affect Consumers' Value Perception During the COVID-19 Pandemic? The Role of Servicescape, Self-Efficacy, and Technological Intervention

This study investigates how physical and psychological distance from one's surroundings may influence one's perception of connectedness with the servicescape and, ultimately, perception of value. It also examines the effect of consumers' techno-psychological differences and interaction modes on this distance-closeness relationship. The researchers develop and test a conceptual framework of how personal cognitive traits and technological intervention may alter consumers' perceived connectedness to the servicescape and influence their perceived value in different service settings. Via a quasi-experiment design in three service scenarios, this research shows a synthetical effect of contactless technology in the distancing setting that may work more effectively on high self-efficiency customers to change their perceived closeness to the servicescape and further change their evaluation of the service. The findings reveal the practical implications of social distancing for different types of consumers in service encounters during or after the COVID-19 pandemic.

Loneliness across time and space

People feel lonely when their social needs are not met by the quantity and quality of their social relationships. Most research has focused on individual-level predictors of loneliness. However, macro-level factors related to historical time and geographic space might influence loneliness through their effects on individual-level predictors. In this Review, we summarize empirical findings on differences in the prevalence of loneliness across historical time and geographical space and discuss four groups of macro-level factors that might account for these differences: values and norms, family and social lives, technology and digitalization, and living conditions and availability of individual resources. Regarding historical time, media reports convey that loneliness is on the rise, but the empirical evidence is mixed, at least before the COVID-19 pandemic. Regarding geographical space, national differences in loneliness are linked to differences in cultural values (such as individualism) but might also be due to differences in the sociodemographic composition of the population. Research on within-country differences in loneliness is scarce but suggests an influence of neighbourhood characteristics. We conclude that a more nuanced understanding of the effects of macro-level factors on loneliness is necessary because of their relevance for public policy and propose specific directions for future research.

The Theoretical Model of Decision-Making Behaviour Geospatial Analysis Using Data Obtained from the Games of Chess

The game of chess offers a conducive setting to explore basic cognitive processes, including decision-making. The game exercises analytical cause-and-effect thinking skills regardless of the level of play. Moreover, chess portals provide information on the chess games played and serve as a vast database. The numbers of games played thus have the potential to be analyzed comprehensively, including for purposes other than analyzing chess matches only. The primary objective of this study is to develop a methodology for using information obtained from chess games for geospatial social analysis. The assumption is that the methodology will allow for general geographical variation in personality inference in the future, relying on big data from chess databases. Future large-scale studies of the geographical differentiation of personality traits using the developed methodology may be applicable in a number of ways. The results can be used wherever cross-sectional social analyses are needed in the context of personality traits (decision-making) to better understand their geographical background. In turn, the geographical distribution of these traits is accompanied by a range of important social, educational, health, political and economic implications.

Thermal demands and its interactions with environmental factors account for national-level variation in aggression

Literature shows that psychological phenomena, including values (e.g., individualism vs. collectivism), personality, and behaviors (e.g., prosocial and aggressive behavior), are geographically clustered. The effects of temperature on interpersonal and intergroup aggression have been studied by many social psychologists. To date the interactions between temperature and other geographical factors have not been addressed. This study is aiming to examine the effects of thermal demands and the moderating effects of natural geographical factors on aggressive behavior at national level. Data for 156 societies was obtained from publicly available databases. Consistent with the life-history theory, results of this study showed that aggressive behavior has a positive relationship with heat demands, and a negative relationship with cold demands. Aggressive behavior is also positively correlated with sunlight and altitude, and negatively correlated with coastline vicinity. Forest, coastline vicinity, and rainfall moderated the effect of thermal demands on aggressive behavior. In societies with more forests, with more rainfall, and closer to coastline, the negative effects of cold demands on aggressive behavior are stronger.

The many geographical layers of culture

Uchiyama et al. present a dual inheritance framework for conceptualizing how behavioural genetics and cultural evolution interact and affect heritability. We posit that to achieve a holistic and nuanced representation of the cultural environment and evolution against which genetic effects should be evaluated, it is imperative to consider the multiple geographic cultural layers impacting individuals and genetic heritability.

Human Personality Is Associated with Geographical Environment in Mainland China

Recent psychological research shown that the places where we live are linked to our personality traits. Geographical aggregation of personalities has been observed in many individualistic nations; notably, the mountainousness is an essential component in understanding regional variances in personality. Could mountainousness therefore also explain the clustering of personality-types in collectivist countries like China? Using a nationwide survey (29,838 participants) in Mainland China, we investigated the relationship between the Big Five personality traits and mountainousness indicators at the provincial level. Multilevel modelling showed significant negative associations between the elevation coefficient of variation (Elevation CV) and the Big Five personality traits, whereas mean elevation (Elevation Mean) and the standard deviation in elevation (Elevation STD) were positively associated with human personalities. Subsequent machine learning analyses showed that, for example, Elevation Mean outperformed other mountainousness indicators regarding correlations with neuroticism, while Elevation CV performed best relative to openness models. Our results mirror some previous findings, such as the positive association between openness and Elevation STD, while also revealing cultural differences, such as the social desirability of people living in China's mountainous areas.

Effective Maps, Easily Done: Visualizing Geo-Psychological Differences Using Distance Weights

Psychologists of many subfields are becoming increasingly interested in the geographical distribution of psychological phenomena. An integral part of this new stream of geo-psychological studies is to visualize spatial distributions of psychological phenomena in maps. However, most psychologists are not trained in visualizing spatial data. As a result, almost all existing geo-psychological studies rely on the most basic mapping technique: color-coding disaggregated data (i.e., grouping individuals into predefined spatial units and then mapping out average scores across these spatial units). Although this basic mapping technique is not wrong, it often leaves unleveraged potential to effectively visualize spatial patterns. The aim of this tutorial is to introduce psychologists to an alternative, easy-to-use mapping technique: distance-based weighting (i.e., calculating area estimates that represent distance-weighted averages of all measurement locations). We outline the basic idea of distance-based weighting and explain how to implement this technique so that it is effective for geo-psychological research. Using large-scale mental-health data from the United States ( N = 2,058,249), we empirically demonstrate how distance-based weighting may complement the commonly used basic mapping technique. We provide fully annotated R code and open access to all data used in our analyses.

Person-Culture Personality Fit: Dispositional Traits and Cultural Context Explain Country-Level Personality Profile Conformity

In general, people are influenced by the standards set forth by groups of others; however, the levels of such conformity vary between people and across cultures. Here, we investigated factors related to country-level personality profile conformity (i.e., person-culture personality fit) across ∼5.9 million participants, residing in 57 different countries. We examined how each of the Big Five personality traits and cultural tightness are associated with variation in person-culture personality fit. We found that scoring higher in Extraversion, Agreeableness, and Conscientiousness and residing in a tight cultural context explains increased personality profile conformity, while scoring higher in Openness and Neuroticism and residing in a loose cultural context explains lower personality profile conformity. Furthermore, we found that Openness and Extraversion interact with cultural context to predict levels of personality profile conformity. These findings reveal that both dispositional and cultural factors correspond to the tendency to conform to country-level norms.

Nonlinear Relationships among the Natural Environment, Health, and Sociodemographic Characteristics across US Counties

Background: The aim of this study was to explore the nonlinear relationships between natural amenities and health at the intersection of sociodemographic characteristics among primary care patients with chronic conditions. Methods: We used survey data from 3409 adults across 119 US counties. PROMIS-29 mental and physical health summary scores were the primary outcomes. The natural environment (measured using the County USDA Natural Amenities Scale (NAS)) was the primary predictor. Piecewise spline regression models were used to explore the relationships between NAS and health at the intersection of sociodemographic factors. Results: We identified a nonlinear relationship between NAS and health. Low-income individuals had a negative association with health with each increase in NAS in high-amenity areas only. However, White individuals had a stronger association with health with each increase in NAS in low-amenity areas. Conclusions: In areas with low natural amenities, more amenities are associated with better physical and mental health, but only for advantaged populations. Meanwhile, for disadvantaged populations, an increase in amenities in high-amenity areas is associated with decreases in mental and physical health. Understanding how traditionally advantaged populations utilize the natural environment could provide insight into the mechanisms driving these disparities.